Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations13138
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)0.1%
Total size in memory6.1 MiB
Average record size in memory485.0 B

Variable types

Categorical8
Numeric8
Text3

Alerts

IPHeaderLength has constant value "20"Constant
Protocol has constant value "6"Constant
TCPUrgentPointer has constant value "0"Constant
Dataset has 11 (0.1%) duplicate rowsDuplicates
AckNumber is highly overall correlated with SequenceNumber and 1 other fieldsHigh correlation
DestPort is highly overall correlated with TCPHeaderLength and 2 other fieldsHigh correlation
IPFlags is highly overall correlated with Label and 5 other fieldsHigh correlation
IPLength is highly overall correlated with SequenceNumber and 2 other fieldsHigh correlation
Label is highly overall correlated with IPFlags and 2 other fieldsHigh correlation
SequenceNumber is highly overall correlated with AckNumber and 3 other fieldsHigh correlation
SourcePort is highly overall correlated with IPFlags and 3 other fieldsHigh correlation
TCPHeaderLength is highly overall correlated with DestPort and 5 other fieldsHigh correlation
TCPLength is highly overall correlated with IPLength and 2 other fieldsHigh correlation
TCPStream is highly overall correlated with AckNumber and 5 other fieldsHigh correlation
TCPflags is highly overall correlated with DestPort and 3 other fieldsHigh correlation
TTL is highly overall correlated with IPFlags and 5 other fieldsHigh correlation
WindowSize is highly overall correlated with DestPort and 1 other fieldsHigh correlation
SequenceNumber has 2156 (16.4%) zerosZeros
AckNumber has 2172 (16.5%) zerosZeros
TCPLength has 4530 (34.5%) zerosZeros
TCPStream has 3685 (28.0%) zerosZeros

Reproduction

Analysis started2024-09-05 13:07:22.998200
Analysis finished2024-09-05 13:07:50.400002
Duration27.4 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Label
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size872.6 KiB
TCP_Camera
8010 
TCP_Assistant
2178 
TCP_Outlet
1820 
TCP_Miscellaneous
944 
TCP_Mobile
 
186

Length

Max length17
Median length10
Mean length11.000304
Min length10

Characters and Unicode

Total characters144522
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTCP_Camera
2nd rowTCP_Camera
3rd rowTCP_Camera
4th rowTCP_Camera
5th rowTCP_Camera

Common Values

ValueCountFrequency (%)
TCP_Camera 8010
61.0%
TCP_Assistant 2178
 
16.6%
TCP_Outlet 1820
 
13.9%
TCP_Miscellaneous 944
 
7.2%
TCP_Mobile 186
 
1.4%

Length

2024-09-05T13:07:50.588411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T13:07:50.953249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tcp_camera 8010
61.0%
tcp_assistant 2178
 
16.6%
tcp_outlet 1820
 
13.9%
tcp_miscellaneous 944
 
7.2%
tcp_mobile 186
 
1.4%

Most occurring characters

ValueCountFrequency (%)
C 21148
14.6%
a 19142
13.2%
T 13138
9.1%
P 13138
9.1%
_ 13138
9.1%
e 11904
8.2%
s 8422
 
5.8%
m 8010
 
5.5%
r 8010
 
5.5%
t 7996
 
5.5%
Other values (10) 20476
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 21148
14.6%
a 19142
13.2%
T 13138
9.1%
P 13138
9.1%
_ 13138
9.1%
e 11904
8.2%
s 8422
 
5.8%
m 8010
 
5.5%
r 8010
 
5.5%
t 7996
 
5.5%
Other values (10) 20476
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 21148
14.6%
a 19142
13.2%
T 13138
9.1%
P 13138
9.1%
_ 13138
9.1%
e 11904
8.2%
s 8422
 
5.8%
m 8010
 
5.5%
r 8010
 
5.5%
t 7996
 
5.5%
Other values (10) 20476
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 21148
14.6%
a 19142
13.2%
T 13138
9.1%
P 13138
9.1%
_ 13138
9.1%
e 11904
8.2%
s 8422
 
5.8%
m 8010
 
5.5%
r 8010
 
5.5%
t 7996
 
5.5%
Other values (10) 20476
14.2%

IPLength
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.44801
Minimum40
Maximum2121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.8 KiB
2024-09-05T13:07:51.288235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile40
Q160
median142
Q3142
95-th percentile281
Maximum2121
Range2081
Interquartile range (IQR)82

Descriptive statistics

Standard deviation93.499149
Coefficient of variation (CV)0.77626144
Kurtosis112.73801
Mean120.44801
Median Absolute Deviation (MAD)0
Skewness8.035895
Sum1582446
Variance8742.0908
MonotonicityNot monotonic
2024-09-05T13:07:51.645348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142 6838
52.0%
52 1821
 
13.9%
60 1366
 
10.4%
40 902
 
6.9%
347 337
 
2.6%
44 336
 
2.6%
86 253
 
1.9%
90 249
 
1.9%
286 221
 
1.7%
137 137
 
1.0%
Other values (98) 678
 
5.2%
ValueCountFrequency (%)
40 902
6.9%
44 336
 
2.6%
52 1821
13.9%
58 1
 
< 0.1%
60 1366
10.4%
64 105
 
0.8%
68 5
 
< 0.1%
75 3
 
< 0.1%
77 34
 
0.3%
78 35
 
0.3%
ValueCountFrequency (%)
2121 1
 
< 0.1%
1864 1
 
< 0.1%
1662 1
 
< 0.1%
1566 3
 
< 0.1%
1500 12
0.1%
1497 2
 
< 0.1%
1420 3
 
< 0.1%
1330 1
 
< 0.1%
1168 1
 
< 0.1%
1145 1
 
< 0.1%

IPHeaderLength
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size757.1 KiB
20
13138 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters26276
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 13138
100.0%

Length

2024-09-05T13:07:52.141674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T13:07:52.567985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
20 13138
100.0%

Most occurring characters

ValueCountFrequency (%)
2 13138
50.0%
0 13138
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 13138
50.0%
0 13138
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 13138
50.0%
0 13138
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 13138
50.0%
0 13138
50.0%

TTL
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.5 KiB
64
11677 
255
1461 

Length

Max length3
Median length2
Mean length2.1112041
Min length2

Characters and Unicode

Total characters27737
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row64
2nd row64
3rd row64
4th row64
5th row64

Common Values

ValueCountFrequency (%)
64 11677
88.9%
255 1461
 
11.1%

Length

2024-09-05T13:07:52.917593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T13:07:53.397785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
64 11677
88.9%
255 1461
 
11.1%

Most occurring characters

ValueCountFrequency (%)
6 11677
42.1%
4 11677
42.1%
5 2922
 
10.5%
2 1461
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 11677
42.1%
4 11677
42.1%
5 2922
 
10.5%
2 1461
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 11677
42.1%
4 11677
42.1%
5 2922
 
10.5%
2 1461
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 11677
42.1%
4 11677
42.1%
5 2922
 
10.5%
2 1461
 
5.3%

Protocol
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size744.3 KiB
6
13138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13138
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
6 13138
100.0%

Length

2024-09-05T13:07:53.745312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T13:07:54.170998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
6 13138
100.0%

Most occurring characters

ValueCountFrequency (%)
6 13138
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 13138
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 13138
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 13138
100.0%

SourcePort
Real number (ℝ)

HIGH CORRELATION 

Distinct941
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43630.214
Minimum3114
Maximum64272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.8 KiB
2024-09-05T13:07:54.555905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3114
5-th percentile5809
Q143932
median45739
Q355778
95-th percentile55778
Maximum64272
Range61158
Interquartile range (IQR)11846

Descriptive statistics

Standard deviation16335.678
Coefficient of variation (CV)0.37441205
Kurtosis1.2438084
Mean43630.214
Median Absolute Deviation (MAD)8854
Skewness-1.5919548
Sum5.7321375 × 108
Variance2.6685437 × 108
MonotonicityNot monotonic
2024-09-05T13:07:55.068082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55778 3659
27.9%
45739 3645
27.7%
43687 318
 
2.4%
36902 314
 
2.4%
54031 264
 
2.0%
36230 143
 
1.1%
56034 120
 
0.9%
45990 119
 
0.9%
36784 83
 
0.6%
55637 81
 
0.6%
Other values (931) 4392
33.4%
ValueCountFrequency (%)
3114 1
 
< 0.1%
3133 1
 
< 0.1%
3158 1
 
< 0.1%
3161 2
< 0.1%
3185 2
< 0.1%
3186 2
< 0.1%
3199 3
< 0.1%
3201 3
< 0.1%
3202 2
< 0.1%
3209 2
< 0.1%
ValueCountFrequency (%)
64272 31
0.2%
64271 1
 
< 0.1%
64270 25
0.2%
64269 9
 
0.1%
60969 3
 
< 0.1%
60941 6
 
< 0.1%
60934 3
 
< 0.1%
60821 1
 
< 0.1%
60801 11
 
0.1%
60578 4
 
< 0.1%

DestPort
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1354.4884
Minimum80
Maximum8883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.8 KiB
2024-09-05T13:07:55.485274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile80
Q1443
median443
Q3443
95-th percentile8443
Maximum8883
Range8803
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2556.691
Coefficient of variation (CV)1.8875696
Kurtosis3.8238784
Mean1354.4884
Median Absolute Deviation (MAD)0
Skewness2.3665655
Sum17795268
Variance6536669
MonotonicityNot monotonic
2024-09-05T13:07:55.694288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
443 9618
73.2%
80 1765
 
13.4%
8443 715
 
5.4%
8883 608
 
4.6%
5104 191
 
1.5%
3475 159
 
1.2%
5223 72
 
0.5%
5224 10
 
0.1%
ValueCountFrequency (%)
80 1765
 
13.4%
443 9618
73.2%
3475 159
 
1.2%
5104 191
 
1.5%
5223 72
 
0.5%
5224 10
 
0.1%
8443 715
 
5.4%
8883 608
 
4.6%
ValueCountFrequency (%)
8883 608
 
4.6%
8443 715
 
5.4%
5224 10
 
0.1%
5223 72
 
0.5%
5104 191
 
1.5%
3475 159
 
1.2%
443 9618
73.2%
80 1765
 
13.4%

SequenceNumber
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5339
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93658.303
Minimum0
Maximum335407
Zeros2156
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size102.8 KiB
2024-09-05T13:07:55.954686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1147
median33649
Q3184194.5
95-th percentile304714.5
Maximum335407
Range335407
Interquartile range (IQR)184047.5

Descriptive statistics

Standard deviation109559.69
Coefficient of variation (CV)1.1697809
Kurtosis-0.87780938
Mean93658.303
Median Absolute Deviation (MAD)33649
Skewness0.77733131
Sum1.2304828 × 109
Variance1.2003326 × 1010
MonotonicityNot monotonic
2024-09-05T13:07:56.288713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2156
 
16.4%
1 879
 
6.7%
308 452
 
3.4%
147 134
 
1.0%
26 77
 
0.6%
51 54
 
0.4%
518 29
 
0.2%
158 22
 
0.2%
2 19
 
0.1%
1668 17
 
0.1%
Other values (5329) 9299
70.8%
ValueCountFrequency (%)
0 2156
16.4%
1 879
6.7%
2 19
 
0.1%
26 77
 
0.6%
27 8
 
0.1%
32 1
 
< 0.1%
34 2
 
< 0.1%
36 2
 
< 0.1%
47 1
 
< 0.1%
51 54
 
0.4%
ValueCountFrequency (%)
335407 1
< 0.1%
335317 1
< 0.1%
335227 1
< 0.1%
335137 1
< 0.1%
335047 1
< 0.1%
334957 1
< 0.1%
334867 1
< 0.1%
334777 1
< 0.1%
334543 1
< 0.1%
334453 1
< 0.1%

AckNumber
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct837
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3025.1563
Minimum0
Maximum31608
Zeros2172
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size102.8 KiB
2024-09-05T13:07:56.610912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q175
median2369
Q35403
95-th percentile7771
Maximum31608
Range31608
Interquartile range (IQR)5328

Descriptive statistics

Standard deviation3085.7584
Coefficient of variation (CV)1.0200327
Kurtosis6.0092235
Mean3025.1563
Median Absolute Deviation (MAD)2368
Skewness1.3943539
Sum39744504
Variance9521905
MonotonicityNot monotonic
2024-09-05T13:07:56.913491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2172
 
16.5%
1 921
 
7.0%
228 328
 
2.5%
847 122
 
0.9%
227 111
 
0.8%
26 81
 
0.6%
149 75
 
0.6%
1111 74
 
0.6%
1555 73
 
0.6%
4293 73
 
0.6%
Other values (827) 9108
69.3%
ValueCountFrequency (%)
0 2172
16.5%
1 921
7.0%
2 12
 
0.1%
26 81
 
0.6%
27 4
 
< 0.1%
32 4
 
< 0.1%
47 1
 
< 0.1%
51 58
 
0.4%
53 4
 
< 0.1%
54 3
 
< 0.1%
ValueCountFrequency (%)
31608 5
< 0.1%
31607 1
 
< 0.1%
31576 1
 
< 0.1%
30674 1
 
< 0.1%
27778 2
 
< 0.1%
24882 1
 
< 0.1%
22913 5
< 0.1%
22912 1
 
< 0.1%
22881 2
 
< 0.1%
21986 1
 
< 0.1%

WindowSize
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4440.495
Minimum0
Maximum65535
Zeros16
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size102.8 KiB
2024-09-05T13:07:57.214497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile406
Q11689
median2529
Q32529
95-th percentile29200
Maximum65535
Range65535
Interquartile range (IQR)840

Descriptive statistics

Standard deviation8509.833
Coefficient of variation (CV)1.9164154
Kurtosis27.646531
Mean4440.495
Median Absolute Deviation (MAD)0
Skewness4.9240869
Sum58339223
Variance72417258
MonotonicityNot monotonic
2024-09-05T13:07:57.552849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2529 7304
55.6%
1500 1345
 
10.2%
5840 1065
 
8.1%
406 1026
 
7.8%
29200 609
 
4.6%
7264 264
 
2.0%
1597 170
 
1.3%
1396 144
 
1.1%
65535 135
 
1.0%
2048 123
 
0.9%
Other values (73) 953
 
7.3%
ValueCountFrequency (%)
0 16
 
0.1%
229 22
 
0.2%
245 113
0.9%
262 2
 
< 0.1%
273 8
 
0.1%
279 18
 
0.1%
296 15
 
0.1%
317 6
 
< 0.1%
318 4
 
< 0.1%
362 29
 
0.2%
ValueCountFrequency (%)
65535 135
 
1.0%
29200 609
 
4.6%
14600 11
 
0.1%
8202 81
 
0.6%
7264 264
 
2.0%
5840 1065
 
8.1%
3632 60
 
0.5%
2529 7304
55.6%
2063 4
 
< 0.1%
2058 16
 
0.1%

TCPHeaderLength
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size757.1 KiB
32
9870 
20
1456 
40
1366 
24
 
336
44
 
110

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters26276
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row32
2nd row40
3rd row32
4th row32
5th row32

Common Values

ValueCountFrequency (%)
32 9870
75.1%
20 1456
 
11.1%
40 1366
 
10.4%
24 336
 
2.6%
44 110
 
0.8%

Length

2024-09-05T13:07:58.118903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T13:07:58.417818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
32 9870
75.1%
20 1456
 
11.1%
40 1366
 
10.4%
24 336
 
2.6%
44 110
 
0.8%

Most occurring characters

ValueCountFrequency (%)
2 11662
44.4%
3 9870
37.6%
0 2822
 
10.7%
4 1922
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 11662
44.4%
3 9870
37.6%
0 2822
 
10.7%
4 1922
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 11662
44.4%
3 9870
37.6%
0 2822
 
10.7%
4 1922
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 11662
44.4%
3 9870
37.6%
0 2822
 
10.7%
4 1922
 
7.3%

TCPLength
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct103
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.050236
Minimum0
Maximum2081
Zeros4530
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size102.8 KiB
2024-09-05T13:07:58.692148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median90
Q390
95-th percentile234
Maximum2081
Range2081
Interquartile range (IQR)90

Descriptive statistics

Standard deviation94.361229
Coefficient of variation (CV)1.3665591
Kurtosis110.66912
Mean69.050236
Median Absolute Deviation (MAD)0
Skewness7.9750964
Sum907182
Variance8904.0415
MonotonicityNot monotonic
2024-09-05T13:07:58.990027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 6838
52.0%
0 4530
34.5%
307 337
 
2.6%
38 254
 
1.9%
234 221
 
1.7%
46 139
 
1.1%
85 137
 
1.0%
69 123
 
0.9%
34 121
 
0.9%
101 49
 
0.4%
Other values (93) 389
 
3.0%
ValueCountFrequency (%)
0 4530
34.5%
4 5
 
< 0.1%
6 1
 
< 0.1%
23 3
 
< 0.1%
25 34
 
0.3%
26 30
 
0.2%
29 1
 
< 0.1%
30 10
 
0.1%
31 9
 
0.1%
33 38
 
0.3%
ValueCountFrequency (%)
2081 1
 
< 0.1%
1824 1
 
< 0.1%
1610 1
 
< 0.1%
1514 3
 
< 0.1%
1460 10
0.1%
1448 2
 
< 0.1%
1445 2
 
< 0.1%
1368 3
 
< 0.1%
1290 1
 
< 0.1%
1116 1
 
< 0.1%

TCPStream
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct501
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.901507
Minimum0
Maximum500
Zeros3685
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size102.8 KiB
2024-09-05T13:07:59.268843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q335
95-th percentile317.15
Maximum500
Range500
Interquartile range (IQR)35

Descriptive statistics

Standard deviation102.54011
Coefficient of variation (CV)2.0968701
Kurtosis6.4011801
Mean48.901507
Median Absolute Deviation (MAD)2
Skewness2.6413335
Sum642468
Variance10514.474
MonotonicityNot monotonic
2024-09-05T13:07:59.601950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3743
28.5%
0 3685
28.0%
1 390
 
3.0%
28 327
 
2.5%
30 322
 
2.5%
4 174
 
1.3%
5 165
 
1.3%
3 137
 
1.0%
8 99
 
0.8%
10 91
 
0.7%
Other values (491) 4005
30.5%
ValueCountFrequency (%)
0 3685
28.0%
1 390
 
3.0%
2 3743
28.5%
3 137
 
1.0%
4 174
 
1.3%
5 165
 
1.3%
6 34
 
0.3%
7 20
 
0.2%
8 99
 
0.8%
9 44
 
0.3%
ValueCountFrequency (%)
500 1
 
< 0.1%
499 5
< 0.1%
498 4
< 0.1%
497 3
< 0.1%
496 4
< 0.1%
495 5
< 0.1%
494 4
< 0.1%
493 5
< 0.1%
492 1
 
< 0.1%
491 1
 
< 0.1%

TCPUrgentPointer
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size744.3 KiB
0
13138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13138
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13138
100.0%

Length

2024-09-05T13:07:59.866651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T13:08:00.113072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13138
100.0%

Most occurring characters

ValueCountFrequency (%)
0 13138
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13138
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13138
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13138
100.0%

IPFlags
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size782.8 KiB
0x40
11677 
0x00
1461 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters52552
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0x40
2nd row0x40
3rd row0x40
4th row0x40
5th row0x40

Common Values

ValueCountFrequency (%)
0x40 11677
88.9%
0x00 1461
 
11.1%

Length

2024-09-05T13:08:00.326510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T13:08:00.608329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0x40 11677
88.9%
0x00 1461
 
11.1%

Most occurring characters

ValueCountFrequency (%)
0 27737
52.8%
x 13138
25.0%
4 11677
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27737
52.8%
x 13138
25.0%
4 11677
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27737
52.8%
x 13138
25.0%
4 11677
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27737
52.8%
x 13138
25.0%
4 11677
22.2%

IPID
Text

Distinct11795
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size808.4 KiB
2024-09-05T13:08:01.072012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters78828
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10826 ?
Unique (%)82.4%

Sample

1st row0xce05
2nd row0x10a4
3rd row0x2835
4th row0xce06
5th row0x2836
ValueCountFrequency (%)
0x0000 355
 
2.7%
0xda86 3
 
< 0.1%
0x30bd 3
 
< 0.1%
0xd9a9 3
 
< 0.1%
0xdc16 3
 
< 0.1%
0x30bb 3
 
< 0.1%
0xd99d 3
 
< 0.1%
0xdc15 3
 
< 0.1%
0xdc14 3
 
< 0.1%
0xda84 3
 
< 0.1%
Other values (11785) 12756
97.1%
2024-09-05T13:08:01.927875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17736
22.5%
x 13138
16.7%
d 6652
 
8.4%
2 4562
 
5.8%
3 4305
 
5.5%
e 3279
 
4.2%
c 2923
 
3.7%
5 2821
 
3.6%
4 2759
 
3.5%
a 2704
 
3.4%
Other values (7) 17949
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17736
22.5%
x 13138
16.7%
d 6652
 
8.4%
2 4562
 
5.8%
3 4305
 
5.5%
e 3279
 
4.2%
c 2923
 
3.7%
5 2821
 
3.6%
4 2759
 
3.5%
a 2704
 
3.4%
Other values (7) 17949
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17736
22.5%
x 13138
16.7%
d 6652
 
8.4%
2 4562
 
5.8%
3 4305
 
5.5%
e 3279
 
4.2%
c 2923
 
3.7%
5 2821
 
3.6%
4 2759
 
3.5%
a 2704
 
3.4%
Other values (7) 17949
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17736
22.5%
x 13138
16.7%
d 6652
 
8.4%
2 4562
 
5.8%
3 4305
 
5.5%
e 3279
 
4.2%
c 2923
 
3.7%
5 2821
 
3.6%
4 2759
 
3.5%
a 2704
 
3.4%
Other values (7) 17949
22.8%
Distinct10867
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Memory size808.4 KiB
2024-09-05T13:08:02.546847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters78828
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9084 ?
Unique (%)69.1%

Sample

1st row0x3b90
2nd row0x7e36
3rd row0x7172
4th row0x3b8f
5th row0x7171
ValueCountFrequency (%)
0xdef7 35
 
0.3%
0xdf10 24
 
0.2%
0xfcff 24
 
0.2%
0xeac1 21
 
0.2%
0xad6d 11
 
0.1%
0xfcf3 10
 
0.1%
0xea34 9
 
0.1%
0xea41 9
 
0.1%
0xea48 9
 
0.1%
0xea78 9
 
0.1%
Other values (10857) 12977
98.8%
2024-09-05T13:08:03.411895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15844
20.1%
x 13138
16.7%
3 6720
 
8.5%
6 6200
 
7.9%
e 3245
 
4.1%
a 3197
 
4.1%
f 3171
 
4.0%
2 3046
 
3.9%
d 2901
 
3.7%
7 2857
 
3.6%
Other values (7) 18509
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15844
20.1%
x 13138
16.7%
3 6720
 
8.5%
6 6200
 
7.9%
e 3245
 
4.1%
a 3197
 
4.1%
f 3171
 
4.0%
2 3046
 
3.9%
d 2901
 
3.7%
7 2857
 
3.6%
Other values (7) 18509
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15844
20.1%
x 13138
16.7%
3 6720
 
8.5%
6 6200
 
7.9%
e 3245
 
4.1%
a 3197
 
4.1%
f 3171
 
4.0%
2 3046
 
3.9%
d 2901
 
3.7%
7 2857
 
3.6%
Other values (7) 18509
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15844
20.1%
x 13138
16.7%
3 6720
 
8.5%
6 6200
 
7.9%
e 3245
 
4.1%
a 3197
 
4.1%
f 3171
 
4.0%
2 3046
 
3.9%
d 2901
 
3.7%
7 2857
 
3.6%
Other values (7) 18509
23.5%

TCPflags
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size808.4 KiB
0x0018
8579 
0x0002
2143 
0x0010
1866 
0x0014
 
340
0x0011
 
181
Other values (2)
 
29

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters78828
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0x0018
2nd row0x0002
3rd row0x0018
4th row0x0018
5th row0x0018

Common Values

ValueCountFrequency (%)
0x0018 8579
65.3%
0x0002 2143
 
16.3%
0x0010 1866
 
14.2%
0x0014 340
 
2.6%
0x0011 181
 
1.4%
0x0004 16
 
0.1%
0x00c2 13
 
0.1%

Length

2024-09-05T13:08:03.741503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T13:08:04.040579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0x0018 8579
65.3%
0x0002 2143
 
16.3%
0x0010 1866
 
14.2%
0x0014 340
 
2.6%
0x0011 181
 
1.4%
0x0004 16
 
0.1%
0x00c2 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 43439
55.1%
x 13138
 
16.7%
1 11147
 
14.1%
8 8579
 
10.9%
2 2156
 
2.7%
4 356
 
0.5%
c 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43439
55.1%
x 13138
 
16.7%
1 11147
 
14.1%
8 8579
 
10.9%
2 2156
 
2.7%
4 356
 
0.5%
c 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43439
55.1%
x 13138
 
16.7%
1 11147
 
14.1%
8 8579
 
10.9%
2 2156
 
2.7%
4 356
 
0.5%
c 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43439
55.1%
x 13138
 
16.7%
1 11147
 
14.1%
8 8579
 
10.9%
2 2156
 
2.7%
4 356
 
0.5%
c 13
 
< 0.1%
Distinct11752
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size808.4 KiB
2024-09-05T13:08:04.590172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters78828
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10556 ?
Unique (%)80.3%

Sample

1st row0x833f
2nd row0x2d43
3rd row0xb236
4th row0xbf7a
5th row0x7364
ValueCountFrequency (%)
0x2775 8
 
0.1%
0x2d9e 7
 
0.1%
0xe0bb 5
 
< 0.1%
0x1fd3 5
 
< 0.1%
0x365b 5
 
< 0.1%
0xc751 5
 
< 0.1%
0x16d9 5
 
< 0.1%
0xfc78 4
 
< 0.1%
0x0039 4
 
< 0.1%
0x03fc 4
 
< 0.1%
Other values (11742) 13086
99.6%
2024-09-05T13:08:05.445921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16506
20.9%
x 13138
16.7%
7 3390
 
4.3%
a 3344
 
4.2%
8 3341
 
4.2%
3 3335
 
4.2%
5 3325
 
4.2%
2 3316
 
4.2%
9 3279
 
4.2%
b 3270
 
4.1%
Other values (7) 22584
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16506
20.9%
x 13138
16.7%
7 3390
 
4.3%
a 3344
 
4.2%
8 3341
 
4.2%
3 3335
 
4.2%
5 3325
 
4.2%
2 3316
 
4.2%
9 3279
 
4.2%
b 3270
 
4.1%
Other values (7) 22584
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16506
20.9%
x 13138
16.7%
7 3390
 
4.3%
a 3344
 
4.2%
8 3341
 
4.2%
3 3335
 
4.2%
5 3325
 
4.2%
2 3316
 
4.2%
9 3279
 
4.2%
b 3270
 
4.1%
Other values (7) 22584
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16506
20.9%
x 13138
16.7%
7 3390
 
4.3%
a 3344
 
4.2%
8 3341
 
4.2%
3 3335
 
4.2%
5 3325
 
4.2%
2 3316
 
4.2%
9 3279
 
4.2%
b 3270
 
4.1%
Other values (7) 22584
28.6%

Interactions

2024-09-05T13:07:46.780208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:29.074964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:31.962587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:34.456191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:36.600407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:39.784983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:42.503376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:44.693480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:47.037646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:29.539354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:32.232590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:34.702777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:36.903855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:40.169765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:42.767195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:44.934654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:47.298951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:29.932208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:32.655706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:34.950327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:37.218785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:40.606205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:43.050512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:45.202844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:47.545018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:30.373860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:32.927829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:35.203074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:37.535116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:40.997280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:43.316041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:45.444079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:47.839035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:30.772877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:33.406310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:35.474154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:37.943443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:41.433755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:43.591670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:45.715932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:48.335477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:31.124776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:33.662113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:35.729501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:38.386533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:41.725544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:43.906337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:46.003870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:48.597274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:31.428289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:33.923043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:36.022817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:38.797863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:41.999288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:44.176551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:46.264936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:48.867387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:31.675820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:34.193343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:36.316675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:39.156483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:42.248468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:44.422885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-05T13:07:46.518080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-09-05T13:08:05.842200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AckNumberDestPortIPFlagsIPLengthLabelSequenceNumberSourcePortTCPHeaderLengthTCPLengthTCPStreamTCPflagsTTLWindowSize
AckNumber1.000-0.1180.3110.4320.2820.8510.2960.2180.472-0.5550.2000.311-0.135
DestPort-0.1181.0000.1380.0060.316-0.1070.2410.555-0.161-0.0560.5160.1380.720
IPFlags0.3110.1381.0000.3060.8820.3530.8820.8960.3070.7020.5151.0000.105
IPLength0.4320.0060.3061.0000.1550.6350.3500.1750.978-0.6100.0680.3060.171
Label0.2820.3160.8820.1551.0000.3990.6420.4850.1580.4310.4300.8820.477
SequenceNumber0.851-0.1070.3530.6350.3991.0000.3890.2860.675-0.7500.2720.3530.035
SourcePort0.2960.2410.8820.3500.6420.3891.0000.5330.293-0.2430.3330.8820.297
TCPHeaderLength0.2180.5550.8960.1750.4850.2860.5331.0000.1770.4160.5480.8960.512
TCPLength0.472-0.1610.3070.9780.1580.6750.2930.1771.000-0.6120.0670.3070.037
TCPStream-0.555-0.0560.702-0.6100.431-0.750-0.2430.416-0.6121.0000.2840.702-0.194
TCPflags0.2000.5160.5150.0680.4300.2720.3330.5480.0670.2841.0000.5150.321
TTL0.3110.1381.0000.3060.8820.3530.8820.8960.3070.7020.5151.0000.105
WindowSize-0.1350.7200.1050.1710.4770.0350.2970.5120.037-0.1940.3210.1051.000

Missing values

2024-09-05T13:07:49.274632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-05T13:07:49.995499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LabelIPLengthIPHeaderLengthTTLProtocolSourcePortDestPortSequenceNumberAckNumberWindowSizeTCPHeaderLengthTCPLengthTCPStreamTCPUrgentPointerIPFlagsIPIDIPchecksumTCPflagsTCPChecksum
0TCP_Camera14220646457394431125293290000x400xce050x3b900x00180x833f
1TCP_Camera6020646467705104005840400100x400x10a40x7e360x00020x2d43
2TCP_Camera14220646557784431125293290200x400x28350x71720x00180xb236
3TCP_Camera142206464573944391125293290000x400xce060x3b8f0x00180xbf7a
4TCP_Camera142206465577844391125293290200x400x28360x71710x00180x7364
5TCP_Camera1422064645739443181125293290000x400xce070x3b8e0x00180xb826
6TCP_Camera522064655778443181752529320200x400x28370x71ca0x00100x4c98
7TCP_Camera14220646557784431817525293290200x400x28380x716f0x00180xbb5c
8TCP_Camera1422064645739443271125293290000x400xce080x3b8d0x00180x2cd5
9TCP_Camera14220646557784432717525293290200x400x28390x716e0x00180x87d9
LabelIPLengthIPHeaderLengthTTLProtocolSourcePortDestPortSequenceNumberAckNumberWindowSizeTCPHeaderLengthTCPLengthTCPStreamTCPUrgentPointerIPFlagsIPIDIPchecksumTCPflagsTCPChecksum
13128TCP_Outlet3472025566075801115002030749800x000xe3550x38bd0x00180xe70f
13129TCP_Outlet40202556607580308227150020049800x000xe3560x39ef0x00140x5a35
13130TCP_Outlet52206463316844300584032049700x400x1d660x98ee0x00020xa508
13131TCP_Outlet52206463316844300584032049700x400x1d670x98ed0x00020xa508
13132TCP_Outlet4420255660768000150024049900x000xe3580x39e90x00020x37b7
13133TCP_Outlet4020255660768011150020049900x000xe3590x39ec0x00100x400a
13134TCP_Outlet3472025566076801115002030749900x000xe35a0x38b80x00180xcacb
13135TCP_Outlet40202556607680308227127420049900x000xe35b0x39ea0x00100x3ed7
13136TCP_Outlet40202556607680308227150020049900x000xe35c0x39e90x00140x3df1
13137TCP_Outlet52206464988844300584032050000x400x86070x473c0x00020x3a2e

Duplicate rows

Most frequently occurring

LabelIPLengthIPHeaderLengthTTLProtocolSourcePortDestPortSequenceNumberAckNumberWindowSizeTCPHeaderLengthTCPLengthTCPStreamTCPUrgentPointerIPFlagsIPIDIPchecksumTCPflagsTCPChecksum# duplicates
0TCP_Assistant5220646642704432237652020473205700x400x00000xdef70x00100x80412
1TCP_Assistant52206466427244313312291320483207200x400x00000xdef70x00100x2fb42
2TCP_Mobile40206465237244387200200500x400x00000xfd0b0x00040x40b82
3TCP_Mobile40206465237344387400200600x400x00000xfd0b0x00040x7ff42
4TCP_Mobile40206465237344387500200600x400x00000xfd0b0x00040x7ff32
5TCP_Mobile522064652368443696138232047320000x400x00000xfcff0x00100xe0ff2
6TCP_Mobile52206465238044316683160820473201400x400x00000xdf100x00100x05612
7TCP_Mobile642064652368443696138232047440000x400x00000xfcf30x00100xa8082
8TCP_Mobile642064652378443923350420484401100x400x00000xbc9f0x00100x25ba2
9TCP_Mobile6420646523844431192347620484401800x400x00000xbcbf0x00100x2a0d2